Comparison of clinical performance of obturating materials in pulpectomies: A randomized clinical trial.

BACKGROUND: Chemomechanical debridement is insufficient to disinfect all bacteria from the root canals of primary teeth, and obturation of canals with an appropriate material thus acquires excellent importance and remains a critical step in the ultimate success of pulpectomy. AIM: The aim of the study was to compare and evaluate Endoflas, Metapex, and a mixture of calcium hydroxide (CH) and zinc oxide (ZnO) as obturating materials (OMs) in primary mandibular second molars. MATERIALS AND METHODS: Seventy-five mandibular second primary molars requiring pulpectomies were identified in children aged 4-8 years. They were randomly allocated to the three treatment groups according to the type of OM received using the block randomization technique. After the completion of chemomechanical debridement, the canals were filled with Endoflas, Metapex, and CH-ZnO mixture, respectively. The intergroup clinical and radiographic comparison was made based on Coll and Sadrian criteria to decipher their clinical performance at 1, 3, and 6 months. RESULTS: No statistically significant differences between the groups were observed at any evaluation time interval (P > 0.05). At 6 months, the clinical success rates were 95.2% in Endoflas, 96% in Metapex, and 95.8% in the CH and ZnO mixture groups, respectively. The materials, however, behaved differently in different clinical situations. CONCLUSION: Based on the observations, all three OMs showed similar clinical success in maintaining tooth functioning, but their use can be restricted to indications. However, prospective studies with longer follow-ups with more stringent eligibility criteria are required to reach more definitive conclusions.

Unveiling the ESR1 Conformational Stability and Screening Potent Inhibitors for Breast Cancer Treatment

BACKGROUND: The current study recognizes the significance of estrogen receptor alpha (ERα) as a member of the nuclear receptor protein family, which holds a central role in the pathophysiology of breast cancer. ERα serves as a valuable prognostic marker, with its established relevance in predicting disease outcomes and treatment responses. METHOD: In this study, computational methods are utilized to search for suitable drug-like compounds that demonstrate analogous ligand binding kinetics to ERα. RESULTS: Docking-based simulation screened out the top 5 compounds - ZINC13377936, NCI35753, ZINC35465238, ZINC14726791, and NCI663569 against the targeted protein. Further, their dynamics studies reveal that the compounds ZINC13377936 and NCI35753 exhibit the highest binding stability and affinity. CONCLUSION: Anticipating the competitive inhibition of ERα protein expression in breast cancer, we envision that both ZINC13377936 and NCI35753 compounds hold substantial promise as potential therapeutic agents. These candidates warrant thorough consideration for rigorous In vitro and In vivo evaluations within the context of clinical trials. The findings from this current investigation carry significant implications for the advancement of future diagnostic and therapeutic approaches for breast cancer.

Exploring the artificial intelligence and machine learning models in the context of drug design difficulties and future potential for the pharmaceutical sectors.

Artificial intelligence (AI), particularly deep learning as a subcategory of AI, provides opportunities to accelerate and improve the process of discovering and developing new drugs. The use of AI in drug discovery is still in its early stages, but it has the potential to revolutionize the way new drugs are discovered and developed. As AI technology continues to evolve, it is likely that AI will play an even greater role in the future of drug discovery. AI is used to identify new drug targets, design new molecules, and predict the efficacy and safety of potential drugs. The inclusion of AI in drug discovery can screen millions of compounds in a matter of hours, identifying potential drug candidates that would have taken years to find using traditional methods. AI is highly utilized in the pharmaceutical industry by optimizing processes, reducing waste, and ensuring quality control. This review covers much-needed topics, including the different types of machine-learning techniques, their applications in drug discovery, and the challenges and limitations of using machine learning in this field. The state-of-the-art of AI-assisted pharmaceutical discovery is described, covering applications in structure and ligand-based virtual screening, de novo drug creation, prediction of physicochemical and pharmacokinetic properties, drug repurposing, and related topics. Finally, many obstacles and limits of present approaches are outlined, with an eye on potential future avenues for AI-assisted drug discovery and design.

Genome analysis of biosurfactant producing bacterium, Bacillus tequilensis.

Bioremediation is crucial for recuperating polluted water and soil. By expanding the surface area of substrates, biosurfactants play a vital role in bioremediation. Biosurfactant-producing microbes release certain biosurfactant compounds, which are promoted for oil spill remediation. In the present investigation, a biosurfactant-producing bacterium Bacillus tequilensis was isolated from Chilika Lake, Odisha, India (latitude and longitude: 19.8450 N 85.4788 E). Whole-Genome Sequencing (WGS) of Bacillus tequilensis was carried out using Illumina NextSeq 500. The size of the whole genome of Bacillus tequilensis was 4.47 MB consisting of 4,478,749 base pairs forming a circular chromosome with 528 scaffolds, 4492 protein-encoding genes (ORFs), 81 tRNA genes, and 114 ribosomal RNA transcription units. The total raw reads were 4209415, and the processed reads were 4058238 with 4492 genes. The whole genome obtained from the present investigation was used for genome annotation, variant calling, variant annotation, and comparative genome analysis with other existing Bacillus species. In this study, a pathway was constructed which describes the biosurfactant metabolism of Bacillus tequilensis. The study identified that genes such as SrfAD, SrfAC, SrfAA and SrfAB are involved in biosurfactant synthesis. The sequence of the genes SrfAD, SrfAC, SrfAA, SrfAB was deposited in GenBank database with accession MUG02427.1, MUG02428.1, MUG02429.1, MUG03515.1 respectively. The whole genome sequence was submitted to GenBank with an accession RMVO00000000 and the raw fastq reads were submitted to SRA, NCBI repository with an accession: SRX5023292.

Promoter-motif extraction from co-regulated genes and their relevance to co-expression using E. coli as a model.

Gene expression varies due to the intrinsic stochasticity of transcription or as a reaction to external perturbations that generate cellular mutations. Co-regulation, co-expression and functional similarity of substances have been employed for indoctrinating the process of the transcriptional paradigm. The difficult process of analysing complicated proteomes and biological switches has been made easier by technical improvements, and microarray technology has flourished as a viable platform. Therefore, this research enables Microarray to cluster genes that are co-expressed and co-regulated into specific segments. Copious search algorithms have been employed to ascertain diacritic motifs or a combination of motifs that are performing regular expression, and their relevant information corresponding to the gene patterns is also documented. The associated genes co-expression and relevant cis-elements are further explored by engaging Escherichia coli as a model organism. Various clustering algorithms have also been used to generate classes of genes with similar expression profiles. A promoter database 'EcoPromDB' has been developed by referring RegulonDB database; this promoter database is freely available at www.ecopromdb.eminentbio.com and is divided into two sub-groups, depending upon the results of co-expression and co-regulation analyses.

Transcriptional regulatory mechanisms and signaling networks in cancer.

Cancer is a general term that refers to a wide range of illnesses that are characterized by the development of aberrant cells that have the capacity to divide uncontrollably, invade, and harm healthy tissue. It is caused by both genetic and epigenetic changes that suppress abnormal proliferation and prevent cells from surviving outside of their normal niches. Complex protein networks are responsible for the development of a suitable environment via multiple cells signaling pathways. The study of these pathways is essential for analysing network context and developing novel cancer therapies. Transcription factors (TFs) are actively involved in gene expression and maintain the combinatorial on-and-off states of the gene. In addition, the TFs regulate cell identity and state; these TFs cooperate to establish cell-type-specific gene expression. In this chapter, we describe the number of transcription factors and their role in the progression of cancer. The knowledge of transcriptional factors and their network is crucial for emphasizing the specific transcriptional addiction and for designing new anticancer therapies.

Structure-Based Virtual Screening, Molecular Docking, Molecular Dynamics Simulation of EGFR for the Clinical Treatment of Glioblastoma.

Glioblastoma (GBM) is a WHO Grade IV tumor with poor visibility, a high risk of comorbidity, and exhibit limited treatment options. Resurfacing from second-rate glioma was originally classified as either mandatory or optional. Recent interest in personalized medicine has motivated research toward biomarker stratification-based individualized illness therapy. GBM biomarkers have been investigated for their potential utility in prognostic stratification, driving the development of targeted therapy and customizing therapeutic treatment. Due to the availability of a specific EGFRvIII mutational variation with a clear function in glioma-genesis, recent research suggests that EGFR has the potential to be a prognostic factor in GBM, while others have shown no clinical link between EGFR and survival. The pre-existing pharmaceutical lapatinib (PubChem ID: 208,908) with a higher affinity score is used for virtual screening. As a result, the current study revealed a newly screened chemical (PubChem CID: 59,671,768) with a higher affinity than the previously known molecule. When the two compounds are compared, the former has the lowest re-rank score. The time-resolved features of a virtually screened chemical and an established compound were investigated using molecular dynamics simulation. Both compounds are equivalent, according to the ADMET study. This report implies that the virtual screened chemical could be a promising Glioblastoma therapy.

SARS-CoV-2 ORF8 dimerization and binding mode analysis with class I MHC: computational approaches to identify COVID-19 inhibitors.

SARS-CoV-2 encodes eight accessory proteins, one of which, ORF8, has a poorly conserved sequence with SARS-CoV and its role in viral pathogenicity has recently been identified. ORF8 in SARS-CoV-2 has a unique functional feature that allows it to form a dimer structure linked by a disulfide bridge between Cys20 and Cys20 (S-S). This study provides structural characterization of natural mutant variants as well as the identification of potential drug candidates capable of binding directly to the interchain disulfide bridge. The lead compounds reported in this work have a tendency to settle in the dimeric interfaces by direct interaction with the disulfide bridge. These molecules may disturb the dimer formation and may have an inhibition impact on its potential functional role in host immune evasion and virulence pathogenicity. This work provides detailed insights on the sequence and structural variability through computational mutational studies, as well as potent drug candidates with the ability to interrupt the intermolecular disulfide bridge formed between Cys20 and Cys20. Furthermore, the interactions of ORF8 peptides complexed with MHC-1 is studied, and the binding mode reveals that certain ORF8 peptides bind to MHC-1 in a manner similar to other viral peptides. Overall, this study is a narrative of various computational approaches used to provide detailed structural insights into SARS-CoV-2 ORF8 interchain disulfide bond disruptors.

Exploring the macromolecules for secretory pathway in cancer disease.

Secretory proteins play an important role in the tumor microenvironment and are widely distributed throughout tumor tissues. Tumor cells secrete a protein that mediates communication between tumor cells and stromal cells, thereby controlling tumor growth and affecting the success of cancer treatments in the clinic. The cancer secretome is produced by various secretory pathways and has a wide range of applications in oncoproteomics. Secretory proteins are involved in cancer development and tumor cell migration, and thus serve as biomarkers or effective therapeutic targets for a variety of cancers. Several proteomic strategies have recently been used for the analysis of cancer secretomes in order to gain a better understanding and elaborate interpretation. For instance, the development of exosome proteomics, degradomics, and tumor-host cell interaction provide clear information regarding the mechanism of cancer pathobiology. In this chapter, we emphasize the recent advances in secretory protein and the challenges in the field of secretome analysis and their clinical applications.

Atom-based 3D-QSAR and DFT analysis of 5-substituted 2-acylaminothiazole derivatives as HIV-1 latency-reversing agents.

HIV-1 latency consists of viral DNA; integrated inside the host genome; it remains transcriptional silent. Combined Antiretroviral Therapy (cART) and the host immune system fail to recognize the latency cells or reservoirs, representing a major barrier to eradicating the HIV-1 infection. The Shock and Kill emerged as a promising strategy to target these cells using Latency reversal agents (LRAs); partially succeeded in producing viral mRNA but failed to reduce the size of reservoirs. In this Context, 2-acylaminothiazole class derivatives appeared as promising HIV-1 latency-reversing agents. In this study, we have developed an atom-based 3 D-QSAR model by utilizing the 49 active compounds of the 5-substituted 2-acylaminothiazoles derivatives. These compounds are further randomly divided into training (37) and test (12) datasets, yielding statistically significant R2 (0.90) and Q2 (0.85) results, respectively. The internal and external validation of the model shows highly robust and reliable results. Next, the model was visualized to check the favourable and unfavourable groups in terms of hydrogen bond donor, electron-withdrawing and hydrophobic group on the most active compound 96 and least active compound 30. The investigated model reveals the structural insights required for obtaining more leads that are potent. Finally, DFT calculations on the most and least active compounds were performed to support the atom-based 3 D-QSAR model. Overall, this study will aid in understanding the minimum structural requirement and functional group required for screening the novel potent leads as HIV-1 latency reversal agents.Communicated by Ramaswamy H. Sarma.

Intrinsically disordered proteins in viral pathogenesis and infections.

Disordered proteins serve a crucial part in many biological processes that go beyond the capabilities of ordered proteins. A large number of virus-encoded proteins have extremely condensed proteomes and genomes, which results in highly disordered proteins. The presence of these IDPs allows them to rapidly adapt to changes in their biological environment and play a significant role in viral replication and down-regulation of host defense mechanisms. Since viruses undergo rapid evolution and have a high rate of mutation and accumulation in their proteome, IDPs' insights into viruses are critical for understanding how viruses hijack cells and cause disease. There are many conformational changes that IDPs can adopt in order to interact with different protein partners and thus stabilize the particular fold and withstand high mutation rates. This chapter explains the molecular mechanism behind viral IDPs, as well as the significance of recent research in the field of IDPs, with the goal of gaining a deeper comprehension of the essential roles and functions played by viral proteins.

Unraveling the multi-targeted curative potential of bioactive molecules against cervical cancer through integrated omics and systems pharmacology approach.

Molecular level understanding on the role of viral infections causing cervical cancer is highly essential for therapeutic development. In these instances, systems pharmacology along with multi omics approach helps in unraveling the multi-targeted mechanisms of novel biologically active compounds to combat cervical cancer. The immuno-transcriptomic dataset of healthy and infected cervical cancer patients was retrieved from the array express. Further, the phytocompounds from medicinal plants were collected from the literature. Network Analyst 3.0 has been used to identify the immune genes around 384 which are differentially expressed and responsible for cervical cancer. Among the 87 compounds reported in plants for treating cervical cancer, only 79 compounds were targeting the identified immune genes of cervical cancer. The significant genes responsible for the domination in cervical cancer are identified in this study. The virogenomic signatures observed from cervical cancer caused by E7 oncoproteins serve as the potential therapeutic targets whereas, the identified compounds can act as anti-HPV drug deliveries. In future, the exploratory rationale of the acquired results will be useful in optimizing small molecules which can be a viable drug candidate.

Viral hijacking mechanism in humans through protein-protein interactions.

Numerous viruses have evolved mechanisms to inhibit or alter the host cell's apoptotic response as part of their coevolution with their hosts. The analysis of virus-host protein interactions require an in-depth understanding of both the viral and host protein structures and repertoires, as well as evolutionary mechanisms and pertinent biological facts. Throughout the course of a viral infection, there is constant battle for binding between virus and cellular proteins. Exogenous interfaces facilitating viral-host interactions are well known for constantly targeting and suppressing endogenous interfaces mediating intraspecific interactions, such as viral-viral and host-host connections. In these interactions, the protein-protein interactions (PPIs), are mostly shown as networks (protein interaction networks, PINs), with proteins represented as nodes and their interactions represented as edges. Host proteins with a higher degree of connectivity are more likely to interact with viral proteins. Due to technical advancements, three-dimensional interactions may now be visualized computationally utilizing molecular modeling and cryo-EM approaches. The uniqueness of viral domain repertoires, their evolution, and their activities during viral infection make viruses fascinating models for research. This chapter aims to provide readers a complete picture of the viral hijacking mechanism in protein-protein interactions.

Integrated Transcriptome Profiling Identifies Prognostic Hub Genes as Therapeutic Targets of Glioblastoma: Evidenced by Bioinformatics Analysis.

Glioblastoma (GBM) is the most devastating and frequent type of primary brain tumor with high morbidity and mortality. Despite the use of surgical resection followed by radio- and chemotherapy as standard therapy, the progression of GBM remains dismal with a median overall survival of <15 months. GBM embodies a populace of cancer stem cells (GSCs) that is associated with tumor initiation, invasion, therapeutic resistance, and post-treatment reoccurrence. However, understanding the potential mechanisms of stemness and their candidate biomarkers remains limited. Hence in this investigation, we aimed to illuminate potential candidate hub genes and key pathways associated with the pathogenesis of GSC in the development of GBM. The integrated analysis discovered differentially expressed genes (DEGs) between the brain cancer tissues (GBM and GSC) and normal brain tissues. Multiple approaches, including gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, were employed to functionally annotate the DEGs and visualize them through the R program. The significant hub genes were identified through the protein-protein interaction network, Venn diagram analysis, and survival analysis. We observed that the upregulated DEGs were prominently involved in the ECM-receptor interaction pathway. The downregulated genes were mainly associated with the axon guidance pathway. Five significant hub genes (CTNNB1, ITGB1, TNC, EGFR, and SHOX2) were screened out through multiple analyses. GO and KEGG analyses of hub genes uncovered that these genes were primarily enriched in disease-associated pathways such as the inhibition of apoptosis and the DNA damage repair mechanism, activation of the cell cycle, EMT (epithelial-mesenchymal transition), hormone AR (androgen receptor), hormone ER (estrogen receptor), PI3K/AKT (phosphatidylinositol 3-kinase and AKT), RTK (receptor tyrosine kinase), and TSC/mTOR (tuberous sclerosis complex and mammalian target of rapamycin). Consequently, the epigenetic regulatory network disclosed that hub genes played a vital role in the progression of GBM. Finally, candidate drugs were predicted that can be used as possible drugs to treat GBM patients. Overall, our investigation offered five hub genes (CTNNB1, ITGB1, TNC, EGFR, and SHOX2) that could be used as precise diagnostic and prognostic candidate biomarkers of GBM and might be used as personalized therapeutic targets to obstruct gliomagenesis.

Firecracker Maxillofacial Injury in a 6-Year-Old Child- A Case Report.

The present case report highlights the management of a 6 years old female child who suffered oral and maxillofacial injury due to explosion of a fire cracker inside the mouth which was managed by primary closure after complete debridement and to prevent the post treatment microstomia, a modified microstomia prevention intraoral prosthetic appliance was given and followed up for 15 months.

Structure and chemistry of enzymatic active sites that play a role in the switch and conformation mechanism.

Enzymes, which are biological molecules, are constructed from polypeptide chains, and these molecules are activated through reaction mechanisms. It is the role of enzymes to speed up chemical reactions that are used to build or break down cell structures. Activation energy is reduced by the enzymes' selective binding of substrates in a protected environment. In enzyme tertiary structures, the active sites are commonly situated in a "cleft," which necessitates the diffusion of substrates and products. The amino acid residues of the active site may be far apart in the primary structure owing to the folding required for tertiary structure. Due to their critical role in substrate binding and attraction, changes in amino acid structure at or near the enzyme's active site usually alter enzyme activity. At the enzyme's active site, or where the chemical reactions occur, the substrate is bound. Enzyme substrates are the primary targets of the enzyme's active site, which is designed to assist in the chemical reaction. This chapter elucidates the summary of structure and chemistry of enzymes, their active site features, charges and role of water in the structures to clarify the biochemistry of the enzymes in the depth of atomic features.

Replantation of a Iatrogenically Avulsed Mandibular Premolar Undergoing Orthodontic Therapy: A 5 Year Follow-Up.

The succedaneous permanent teeth develop in close proximity to primary teeth. They can get accidentally luxated or avulsed during the extraction of primary teeth. The purpose of this paper was to describe a case of a 14-year-old boy with an "iatrogenic avulsion" of an immature mandibular second premolar during the extraction of a primary mandibular second molar. The case was managed successfully with replantation technique within 30 minutes of extra oral period and followed up for 5 years. The replanted tooth remained clinically asymptomatic, showed continued root development and eruption and remained vital. This paper had also discussed about the modifications in extraction technique to avoid the iatrogenic avulsion of permanent tooth bud during extraction of primary teeth.

Structure-Based Virtual Screening, Molecular Docking, and Molecular Dynamics Simulation of VEGF inhibitors for the clinical treatment of Ovarian Cancer.

Vascular endothelial growth factor (VEGF) and its receptor play an important role both in physiologic and pathologic angiogenesis, which is identified in ovarian cancer progression and metastasis development. The aim of the present investigation is to identify a potential vascular endothelial growth factor inhibitor which is playing a crucial role in stimulating the immunosuppressive microenvironment in tumor cells of the ovary and to examine the effectiveness of the identified inhibitor for the treatment of ovarian cancer using various in silico approaches. Twelve established VEGF inhibitors were collected from various literatures. The compound AEE788 displays great affinity towards the target protein as a result of docking study. AEE788 was further used for structure-based virtual screening in order to obtain a more structurally similar compound with high affinity. Among the 80 virtual screened compounds, CID 88265020 explicates much better affinity than the established compound AEE788. Based on molecular dynamics simulation, pharmacophore and comparative toxicity analysis of both the best established compound and the best virtual screened compound displayed a trivial variation in associated properties. The virtual screened compound CID 88265020 has a high affinity with the lowest re-rank score and holds a huge potential to inhibit the VGFR and can be implemented for prospective future investigations in ovarian cancer.

Immunological insights of selectins in human disease mechanism.

Selectin enzymes are glycoproteins and are an important adhesion molecule in the mammalian immune system, especially in the inflammatory response and the healing process of tissues. Selectins play an important role in a variety of biological processes, including the rolling of leukocytes in endothelial cells, a process known as the adhesion cascade. It has recently been discovered and reported that the selectin mechanism plays a role in cancer and thrombosis disease. This process begins with non-covalent interactions-based selectin-ligand binding and the glycans play a role as a connector between cancer cells and the endothelium in this process. The selectin mechanism is critical for the immune system, but it is also involved in disease mechanisms, earning the selectins the nickname "Selectins-The Two Dr. Jekyll and Mr. Hyde Faces". As a result, the drug for selectins should have a multifaceted role and be a dynamic molecule that targets the disease mechanism specifically. This chapter explores the role of selectins in the disease mechanism at the mechanism level that provides the impact for identifying the selectin inhibitors. Overall, this chapter provides the molecular level insights on selectins, their ligands, involvement in normal and disease mechanisms.